Literature DB >> 31740339

Human cortical networking by probabilistic and frequency-specific coupling.

Yuxiang Yan1, Tianyi Qian1, Xin Xu2, Hao Han1, Zhipei Ling2, Wenjin Zhou3, Hesheng Liu4, Bo Hong5.   

Abstract

Large-scale cortical networking patterns have been established based on the correlation of slow fluctuations of resting fMRI signals. However, the electrophysiological mechanism of cortical networking remained to be elucidated. With large-scale human ECoG recording, we developed a novel approach for functional network parcellation on the basis of probabilistic co-activation of cortical sites in spatio-temporal microstates. The parcellated networks were verified by electrical cortical stimulation (ECS) and somatosensory evoked potentials recording, which showed significantly higher accuracy than the traditional long-term correlation method. This provides direct electrophysiological evidence supporting the dynamic nature of cortical networking. Further analysis revealed that the brain-wide connectivity is likely established on the coupling of ECoG power envelop over a common carrier frequency ranging from alpha to low-beta (8-32Hz). Surprisingly, the cortical networking pattern over this specific frequency was found to be consistent across various tasks, which resembles the resting networks. The high similarity between the above functional network parcellation and the fMRI resting network atlas in individuals also suggested the slow power-envelope coupling of band-limited neural oscillations as the electrophysiological basis of spontaneous BOLD signals. Collectively, our findings on direct human recording revealed a probabilistic and frequency specific coupling mechanism for large-scale cortical networking shared by task and resting brain.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Carrier frequency; Dynamic connectivity; ECoG; Functional network; Resting state

Mesh:

Year:  2019        PMID: 31740339     DOI: 10.1016/j.neuroimage.2019.116363

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  4 in total

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Journal:  Front Neurosci       Date:  2021-12-07       Impact factor: 4.677

  4 in total

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